Analyzing Middle School Students’ Distance Education Experiences in COVID-19 via Sentiment Analysis and Topic Modeling

Ekrem Bahçekapılı; Bülent Kandemir; Elif Baykal Kablan

doi:10.19173/irrodl.v27i1.8920

Authors

Ekrem Bahçekapılı Department of Management Information Systems, Karadeniz Technical University https://orcid.org/0000-0002-7538-1712
Bülent Kandemir Department of Educational Sciences, Ordu University https://orcid.org/0000-0002-2852-547X
Elif Baykal Kablan Department of Software Engineering, Karadeniz Technical University https://orcid.org/0000-0003-3552-638X

DOI:

https://doi.org/10.19173/irrodl.v27i1.8920

Keywords:

COVID-19, NLP, student emotion, student perception, sentiment analysis, topic modeling

Abstract

This study investigated middle school students’ experiences with emergency remote education during the COVID-19 pandemic using natural language processing (NLP), sentiment analysis, and topic modeling techniques. A total of 2,739 valid responses from Turkish students (ages 9–15) were collected through open-ended survey questions regarding the perceived advantages and disadvantages of distance learning. Sentiment classification was performed using a semi-supervised machine learning approach, combining TF-IDF, Word2Vec, and FastText vectorization with five classification algorithms. The TF-IDF + support vector machines (SVM) combination yielded the highest performance (F1 = 0.85). Results show a total of 1,867 positive and 2,542 negative opinions, indicating that students generally adopted a more critical view of distance education. To explore the thematic structure of opinions, topic modeling was applied with six topics. Positive sentiments clustered around themes such as educational continuity, health protection, time savings, flexible scheduling, self-regulated learning, and digital literacy. Negative sentiments were dominated by themes including limited interaction, screen fatigue, perceived low quality, technical barriers, and structural inequalities. Findings suggest that while students appreciated the safety and flexibility of remote learning, they also faced significant pedagogical, physical, and technological challenges. The study contributes methodologically by demonstrating the effectiveness of AI-based text analysis and offers practical implications for designing more equitable and student-centered digital education models. These results underscore the importance of integrating NLP and machine learning tools into educational research to uncover deeper insights from student-generated content at scale.

References

Adams, D., Cheah, K. S. L., Thien, L. M., & Md Yusoff, N. N. (2024). Leading schools through the COVID-19 crisis in a South-East Asian country. Management in Education, 38(2), 72–78. https://doi.org/10.1177/08920206211037738

Akhmedov, F., Abdusalomov, A., Makhmudov, F., & Cho, Y. I. (2021). LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model. Applied Sciences, 11(23), Article 11091. https://doi.org/10.3390/app112311091

Albert, A. (2024). From classrooms to confinement: Academic challenges faced by secondary school children in Kyamuhunga Sub-County during COVID-19. International Journal of Innovative Science and Research Technology, 9(9), 3206–3211. https://doi.org/10.38124/ijisrt/IJISRT24SEP1074

Al Husaeni, D. F., Haristiani, N., Wahyudin, W., & Rasim, R. (2022). Chatbot artificial intelligence as educational tools in science and engineering education: A literature review and bibliometric mapping analysis with its advantages and disadvantages. ASEAN Journal of Science and Engineering, 4(1), 93–118. https://doi.org/10.17509/ajse.v4i1.67429

Alweshah, M. (2019). Construction biogeography-based optimization algorithm for solving classification problems. Neural Computing and Applications, 31(10), 5679–5688. https://doi.org/10.1007/s00521-018-3402-8

Amaro, A., & Bacao, F. (2024). Topic modeling: A consistent framework for comparative studies. Emerging Science Journal, 8(1), 125–139. https://doi.org/10.28991/ESJ-2024-08-01-09

An, X., Chai, C. S., Li, Y., Zhou, Y., & Yang, B. (2023). Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 38(5-6), 987–1008. https://doi.org/10.1080/09588221.2023.2246519

Baral, S., Botelho, A. F., Santhanam, A., Gurung, A., Erickson, J., & Heffernan, N. T. (2023). Investigating patterns of tone and sentiment in teacher written feedback messages. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial intelligence in education. Posters and late breaking results, Workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium and blue sky (CCIS, Vol. 1831, pp. 341–346). Springer. https://doi.org/10.1007/978-3-031-36336-8_53

Bittencourt, I. I., Chalco, G., Santos, J., Fernandes, S., Silva, J., Batista, N., Hutz, C., & Isotani, S. (2024). Positive artificial intelligence in education (P-AIED): A roadmap. International Journal of Artificial Intelligence in Education, 34(3), 732–792. https://doi.org/10.1007/s40593-023-00357-y

Borazon, E. Q., Marques, S., & Saycon, D. R. (2024, November 14). E-learning adoption: A comparative analysis of public sentiments during COVID-19. Information Technology for Development, 1–29. https://doi.org/10.1080/02681102.2024.2423286

Caviola, S., Visentin, C., Borella, E., Mammarella, I., & Prodi, N. (2021). Out of the noise: Effects of sound environment on maths performance in middle-school students. Journal of Environmental Psychology, 73, Article 101552. https://doi.org/10.1016/j.jenvp.2021.101552

Copur-Gencturk, Y., Li, J., Cohen, A. S., & Orrill, C. H. (2024). The impact of an interactive, personalized computer-based teacher professional development program on student performance: A randomized controlled trial. Computers & Education, 210, Article 104963. https://doi.org/10.1016/j.compedu.2023.104963

Datta, P., & Nwankpa, J. K. (2021). Digital transformation and the COVID-19 crisis continuity planning. Journal of Information Technology Teaching Cases, 11(2), 81–89. https://doi.org/10.1177/2043886921994821

Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44–49. https://doi.org/10.1016/j.procs.2016.05.124

Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018

Diz-Otero, M., Portela-Pino, I., Domínguez-Lloria, S., & Pino-Juste, M. (2023). Digital competence in secondary education teachers during the COVID-19-derived pandemic: Comparative analysis. Education + Training, 65(2), 181–192. https://doi.org/10.1108/ET-01-2022-0001

Dokuchyna, T. (2023). Students’ attitude to distance learning as a component of their mobility. Pedagogy, 95(4), 439–448. https://doi.org/10.53656/ped2023-4.02

Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225–241. https://doi.org/10.1111/j.1532-7795.2010.00725.x

Fang, B., Li, X., Han, G., & He, J. (2023). Facial expression recognition in educational research from the perspective of machine learning: A systematic review. IEEE Access, 11, 112060–112074. https://doi.org/10.1109/ACCESS.2023.3322454

Gallagher, R. J., Reing, K., Kale, D., & Ver Steeg, G. (2017). Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics, 5, 529–542. https://doi.org/10.1162/tacl_a_00078

Garcia, M. (2021). Embeddings in natural language processing: Theory and advances in vector representations of meaning [Review of the book Embeddings in natural language processing: Theory and advances in vector representations of meaning, by M. T. Pilehvar & J. Camacho-Collados]. Computational Linguistics, 47(3), 699–701. https://doi.org/10.1162/coli_r_00410

Garrison, D. R. (2016). E-learning in the 21st century: A community of inquiry framework for research and practice (3rd ed.). Routledge. https://doi.org/10.4324/9781315667263

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6

Gerlach, M., Peixoto, T. P., & Altmann, E. G. (2018). A network approach to topic models. Science Advances, 4(7), Article eaaq1360. https://doi.org/10.1126/sciadv.aaq1360

Geulayov, G., Mansfield, K., Jindra, C., Hawton, K., & Fazel, M. (2024). Loneliness and self-harm in adolescents during the first national COVID-19 lockdown: Results from a survey of 10,000 secondary school pupils in England. Current Psychology, 43(15), 14063–14074. https://doi.org/10.1007/s12144-022-03651-5

Gurcan, F., Ozyurt, O., & Cagitay, N. E. (2021). Investigation of emerging trends in the e-learning field using latent Dirichlet allocation. The International Review of Research in Open and Distributed Learning, 22(2), 1–18. https://doi.org/10.19173/irrodl.v22i2.5358

Hurling, A., Ballard, H., & Whitaker, A. (2024). The effects of COVID-19 on public secondary schools operating in various economic environments. A case study of schools in Metros South and Central Schools, Western Cape. American Journal of Education and Practice, 8(5), 18–29. https://doi.org/10.47672/ajep.2471

Kadirhan, Z., & Sat, M. (2024). K–12 teachers’ perceived experiences with distance education during the COVID-19 pandemic: A meta-synthesis study. Turkish Online Journal of Distance Education, 25(3), 57–75. https://doi.org/10.17718/tojde.1320633

Khanna, A., & Coumans, J.-V. (2023). Distributed vector representations of neurosurgical admission notes predict discharge disposition. Neurosurgery, 69(Suppl_1), 47. https://doi.org/10.1227/neu.0000000000002375_322

Lee, K., Kim, T.-J., Cefa Sari, B., & Bozkurt, A. (2023). Shifting conversations on online distance education in South Korean society during the COVID-19 pandemic: A topic modeling analysis of news articles. The International Review of Research in Open and Distributed Learning, 24(3), 125–144. https://doi.org/10.19173/irrodl.v24i3.7220

Leech, N. L., Gullett, S., Cummings, M. H., & Haug, C. A. (2022). The challenges of remote K–12 education during the COVID-19 pandemic: Differences by grade level. Online Learning, 26(1), 245–267. https://doi.org/10.24059/olj.v26i1.2609

Lin, H., & Chen, Q. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychology, 12(1), Article 487. https://doi.org/10.1186/s40359-024-01979-0

Lin, L., Zhou, D., Wang, J., & Wang, Y. (2024). A systematic review of big data driven education evaluation. Sage Open, 14(2), Article 21582440241242180. https://doi.org/10.1177/21582440241242180

Lo, C. K., Cheung, K. L., Chan, H. R., & Chau, C. L. E. (2023). Developing flipped learning resources to support secondary school mathematics teaching during the COVID-19 pandemic. Interactive Learning Environments, 31(8), 4787–4805. https://doi.org/10.1080/10494820.2021.1981397

Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522–526. https://doi.org/10.1093/fampra/13.6.522

Martin, F., Sun, T., Westine, C. D., & Ritzhaupt, A. D. (2022). Examining research on the impact of distance and online learning: A second-order meta-analysis study. Educational Research Review, 36, Article 100438. https://doi.org/10.1016/j.edurev.2022.100438

Martínez-Comesaña, M., Rigueira-Díaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J., Ocarranza-Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review. Revista de Psicodidáctica (English Ed.), 28(2), 93–103. https://doi.org/10.1016/j.psicoe.2023.06.002

Mifrah, S., & Benlahmar, E. H. (2020). Topic modeling coherence: A comparative study between LDA and NMF models using COVID’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering, 9(4), 5756–5761. https://doi.org/10.30534/ijatcse/2020/231942020

Mishra, R. K., Urolagin, S., Jothi, J. A. A., Neogi, A. S., & Nawaz, N. (2021). Deep learning-based sentiment analysis and topic modeling on tourism during COVID-19 pandemic. Frontiers in Computer Science, 3, Article 775368. https://doi.org/10.3389/fcomp.2021.775368

Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22–38). Routledge.

Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), Article 8438. https://doi.org/10.3390/app11188438

Naidu, S. (2019). The changing narratives of open, flexible and online learning. Distance Education, 40(2), 149–152. https://doi.org/10.1080/01587919.2019.1612981

Nowak, K. L., & Watt, J. H. (2022). Distance education during the COVID-19 shutdown: A process model of online learner readiness, experiences and feelings of learning. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), Article 293. https://doi.org/10.1145/3555184

Onan, A. (2021). Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589. https://doi.org/10.1002/cae.22253

Ozyurt, O., & Ayaz, A. (2022). Twenty-five years of education and information technologies: Insights from a topic modeling based bibliometric analysis. Education and Information Technologies, 27(8), 11025–11054. https://doi.org/10.1007/s10639-022-11071-y

Özyurt, B., & Akcayol, M. A. (2020). A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA. Expert Systems with. Applications, 168, Article 114231. https://doi.org/10.1016/j.eswa.2020.114231

Patil, R., Boit, S., Gudivada, V., & Nandigam, J. (2023). A survey of text representation and embedding techniques in NLP. IEEE Access, 11, 36120–36146. https://doi.org/10.1109/ACCESS.2023.3266377

Pelaez, A., Jacobson, A., Trias, K., & Winston, E. (2022). The Turing teacher: Identifying core attributes for AI learning in K–12. Frontiers in Artificial Intelligence, 5, Article 1031450. https://doi.org/10.3389/frai.2022.1031450

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Radwan, E., Radwan, A., & Radwan, W. (2020). The role of social media in spreading panic among primary and secondary school students during the COVID-19 pandemic: An online questionnaire study from the Gaza Strip, Palestine. Heliyon, 6(12), Article e05807. https://doi.org/10.1016/j.heliyon.2020.e05807

Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F., & Cuenca-Jiménez, P.-M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, 223, Article 119862. https://doi.org/10.1016/j.eswa.2023.119862

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

Sandvik, L. V., Smith, K., Strømme, A., Svendsen, B., Aasmundstad Sommervold, O., & Aarønes Angvik, S. (2024). Students’ perceptions of assessment practices in upper secondary school during COVID-19. Teachers and Teaching, 30(7–8), 918–931. https://doi.org/10.1080/13540602.2021.1982692

Scully, D., Lehane, P., & Scully, C. (2021). “It is no longer scary”: Digital learning before and during the COVID-19 pandemic in Irish secondary schools. Technology, Pedagogy and Education, 30(1), 159–181. https://doi.org/10.1080/1475939X.2020.1854844

Solano-Gutiérrez, G. A. (2024). La tecnología en la educación a distancia: Revisión de progresos y obstáculos a supercar [Technology in distance education: A review of progress and obstacles]. Revista Científica Zambos, 3(2), 48–73. https://doi.org/10.69484/rcz/v3/n2/17

Tomaszewski, W., Zajac, T., Rudling, E., Te Riele, K., McDaid, L., & Western, M. (2023). Uneven impacts of COVID‐19 on the attendance rates of secondary school students from different socioeconomic backgrounds in Australia: A quasi‐experimental analysis of administrative data. Australian Journal of Social Issues, 58(1), 111–130. https://doi.org/10.1002/ajs4.219

Tuguic, L. A., & Bilan, H. P. (2023). College education students’ learning experiences on the advent of online distance education in the Philippines: A phenomenological study. The Qualitative Report, 28(7), 1869–1879 https://doi.org/10.46743/2160-3715/2023.5644

Tzimiris, S., Nikiforos, S., & Kermanidis, K. L. (2023). Post-pandemic pedagogy: Emergency remote teaching impact on students with functional diversity. Education and Information Technologies, 28(8), 10285–10328. https://doi.org/10.1007/s10639-023-11582-2

Uçar, S.-Ş., Aldabe, I., Aranberri, N., & Arruarte, A. (2024). Exploring automatic readability assessment for science documents within a multilingual educational context. International Journal of Artificial Intelligence in Education, 34(4), 1417–1459. https://doi.org/10.1007/s40593-024-00393-2

Upadhyay, H., Juneja, S., Juneja, A., Dhiman, G., & Kautish, S. (2021). Evaluation of ergonomics‐related disorders in online education using fuzzy AHP. Computational Intelligence and Neuroscience, 2021(1), Article 2214971. https://doi.org/10.1155/2021/2214971

van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004

Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, Article 101582. https://doi.org/10.1016/j.is.2020.101582

Virtanen, S., & Girolami, M. (2019). Precision–recall balanced topic modelling. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 32, pp. 6747–6756). Curran Associates, Inc. https://papers.nips.cc/paper_files/paper/2019/hash/310cc7ca5a76a446f85c1a0d641ba96d-Abstract.html

Waheeb, S. A., Khan, N. A., & Shang, X. (2022). Topic modeling and sentiment analysis of online education in the COVID-19 era using social networks based datasets. Electronics, 11(5), Article 715. https://doi.org/10.3390/electronics11050715

Xing, W., Li, H., Kim, T., Zhu, W., & Song, Y. (2025). Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling. Education and Information Technologies, 30(5), 5561–5588. https://doi.org/10.1007/s10639-024-13038-7

Xu, H., Gan, W., Qi, Z., Wu, J., & Yu, P. S. (2024). Large language models for education: A survey. arXiv. arXiv:2405.13001. https://doi.org/10.48550/arXiv.2405.13001

Zarkadoulas, D., & Virvou, M. (2024). Emotional expression in mathematics e-learning using emojis: A gender-based analysis. Intelligent Decision Technologies, 18(2), 1181–1201. https://doi.org/10.3233/IDT-240170

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Žitnik, S., & Smith, G. G. (2024). Automated analysis of postings in fourth grade online discussions to help teachers keep students on-track. Interactive Learning Environments, 32(8), 4587–4612. https://doi.org/10.1080/10494820.2023.2204327